{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作者：董汇标MINUS 知乎专栏地址：https://zhuanlan.zhihu.com/p/97217536"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>birthday</th>\n",
       "      <th>company</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>杨效丰</td>\n",
       "      <td>1972-12</td>\n",
       "      <td>北京利德华福电气技术有限公司</td>\n",
       "      <td>122.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>纪丰伟</td>\n",
       "      <td>1974-12</td>\n",
       "      <td>北京航天数据股份有限公司</td>\n",
       "      <td>121.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>王永</td>\n",
       "      <td>1974-05</td>\n",
       "      <td>品牌联盟(北京)咨询股份公司</td>\n",
       "      <td>118.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>杨静</td>\n",
       "      <td>1975-07</td>\n",
       "      <td>中科专利商标代理有限责任公司</td>\n",
       "      <td>118.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>张凯江</td>\n",
       "      <td>1974-11</td>\n",
       "      <td>北京阿里巴巴云计算技术有限公司</td>\n",
       "      <td>117.79</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name birthday          company   score\n",
       "id                                       \n",
       "1   杨效丰  1972-12   北京利德华福电气技术有限公司  122.59\n",
       "2   纪丰伟  1974-12     北京航天数据股份有限公司  121.25\n",
       "3    王永  1974-05   品牌联盟(北京)咨询股份公司  118.96\n",
       "4    杨静  1975-07   中科专利商标代理有限责任公司  118.21\n",
       "5   张凯江  1974-11  北京阿里巴巴云计算技术有限公司  117.79"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "luohu_data = pd.read_csv('./bj_luohu.csv', index_col = 'id')\n",
    "luohu_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>公司名</th>\n",
       "      <th>人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABB（中国）有限公司</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BONNIER图书文化（北京）有限公司</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IGT科技开发（北京）有限公司</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>POLYCOM通讯技术（北京）有限公司</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VIDIA技术服务（北京）有限公司</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>《中国企业报》股份有限公司</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>《中国当代医药》杂志社有限公司</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>《中国汽车报》社有限公司</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>《中国烟草》杂志社有限公司</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>《中国经济周刊》杂志社</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>《中国质量万里行》杂志社</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>《中国邮政报》社</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>《中国饲料》杂志社</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>《信息早报》社</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>《环境与生活》杂志社有限公司</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>《环球时报》社</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>《证券日报》社</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>一汽丰田汽车销售有限公司</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>万国数据服务有限公司北京分公司</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>万图（北京）科技有限公司</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    公司名  人数\n",
       "0           ABB（中国）有限公司   8\n",
       "1   BONNIER图书文化（北京）有限公司   1\n",
       "2       IGT科技开发（北京）有限公司   1\n",
       "3   POLYCOM通讯技术（北京）有限公司   2\n",
       "4     VIDIA技术服务（北京）有限公司   1\n",
       "5         《中国企业报》股份有限公司   2\n",
       "6       《中国当代医药》杂志社有限公司   1\n",
       "7          《中国汽车报》社有限公司   1\n",
       "8         《中国烟草》杂志社有限公司   1\n",
       "9           《中国经济周刊》杂志社   1\n",
       "10         《中国质量万里行》杂志社   1\n",
       "11             《中国邮政报》社   1\n",
       "12            《中国饲料》杂志社   1\n",
       "13              《信息早报》社   1\n",
       "14       《环境与生活》杂志社有限公司   1\n",
       "15              《环球时报》社   5\n",
       "16              《证券日报》社   1\n",
       "17         一汽丰田汽车销售有限公司  12\n",
       "18      万国数据服务有限公司北京分公司   1\n",
       "19         万图（北京）科技有限公司   1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_data = luohu_data.groupby('company', as_index=False).count()[['company', 'name']]\n",
    "company_data.rename(columns={'name':'人数'}, inplace=True)      # 替换 1\n",
    "company_data.rename(columns={'company':'公司名'},inplace=True)  # 替换 2\n",
    "company_data.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>公司名</th>\n",
       "      <th>人数</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>北京华为数字技术有限公司</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>中央电视台</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2457</th>\n",
       "      <td>北京首钢建设集团有限公司</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3082</th>\n",
       "      <td>百度在线网络技术（北京）有限公司</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3194</th>\n",
       "      <td>联想（北京）有限公司</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>中国民生银行股份有限公司</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2648</th>\n",
       "      <td>国际商业机器（中国）投资有限公司</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1334</th>\n",
       "      <td>北京外企人力资源服务有限公司</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>中国国际技术智力合作有限公司</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2514</th>\n",
       "      <td>华为技术有限公司北京研究所</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3049</th>\n",
       "      <td>爱立信（中国）通信有限公司</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3201</th>\n",
       "      <td>腾讯科技（北京）有限公司</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2405</th>\n",
       "      <td>北京阿里巴巴云计算技术有限公司</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>中国石油天然气股份有限公司管道北京输油气分公司</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3070</th>\n",
       "      <td>用友软件股份有限公司</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          公司名   人数\n",
       "1081             北京华为数字技术有限公司  137\n",
       "312                     中央电视台   73\n",
       "2457             北京首钢建设集团有限公司   57\n",
       "3082         百度在线网络技术（北京）有限公司   55\n",
       "3194               联想（北京）有限公司   48\n",
       "214              中国民生银行股份有限公司   40\n",
       "2648         国际商业机器（中国）投资有限公司   39\n",
       "1334           北京外企人力资源服务有限公司   39\n",
       "174            中国国际技术智力合作有限公司   29\n",
       "2514            华为技术有限公司北京研究所   27\n",
       "3049            爱立信（中国）通信有限公司   26\n",
       "3201             腾讯科技（北京）有限公司   24\n",
       "2405          北京阿里巴巴云计算技术有限公司   23\n",
       "240   中国石油天然气股份有限公司管道北京输油气分公司   20\n",
       "3070               用友软件股份有限公司   20"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "company_sorted_data = company_data.sort_values('人数', ascending=False)\n",
    "company_sorted_data.head(15)   # 前15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "score\n",
       "(90, 95]      3344\n",
       "(95, 100]     1757\n",
       "(100, 105]     653\n",
       "(105, 110]     218\n",
       "(110, 115]      32\n",
       "(115, 120]      13\n",
       "(120, 125]       2\n",
       "Name: score, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cut_bins = np.arange(90, 130, 5)   #分段设置\n",
    "bins = pd.cut(luohu_data['score'], cut_bins)  # 将落户数据，按照cutbins来切一下\n",
    "bin_counts = luohu_data['score'].groupby(bins).count()\n",
    "bin_counts.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\font_manager.py:1241: UserWarning: findfont: Font family ['Arial Unicode MS'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "plt.rcParams['font.family']='Arial Unicode MS'\n",
    "plt.rcParams['axes.unicode_minus']=False\n",
    "plt.rcParams['font.size']=12\n",
    "\n",
    "x_name=['90-95','95-100','100-105','105-110','110-115','115-120','120-125']\n",
    "sns.barplot(x_name,bin_counts)\n",
    "plt.ylabel('人数')\n",
    "plt.xlabel('分数区间')\n",
    "\n",
    "for x, y in zip(range(7), bin_counts):\n",
    "    plt.text(x, y+20 , y, ha='center', va='bottom')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>score</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6019.000000</td>\n",
       "      <td>6019.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>95.654552</td>\n",
       "      <td>41.960493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.354445</td>\n",
       "      <td>2.814382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>90.750000</td>\n",
       "      <td>32.852055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>92.330000</td>\n",
       "      <td>40.027397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>94.460000</td>\n",
       "      <td>41.942466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>97.750000</td>\n",
       "      <td>43.945205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>122.590000</td>\n",
       "      <td>60.041096</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             score          age\n",
       "count  6019.000000  6019.000000\n",
       "mean     95.654552    41.960493\n",
       "std       4.354445     2.814382\n",
       "min      90.750000    32.852055\n",
       "25%      92.330000    40.027397\n",
       "50%      94.460000    41.942466\n",
       "75%      97.750000    43.945205\n",
       "max     122.590000    60.041096"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "luohu_data['age'] = (pd.to_datetime('2018-07') - pd.to_datetime(luohu_data['birthday'])) / pd.Timedelta('365 days')\n",
    "luohu_data.describe()\n",
    "#luohu_data['score'].mean() >95"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均值 41.960493141581246\n",
      "标准差 2.814381679708802\n",
      "下限 33.51734810245484\n",
      "上限 50.40363818070765\n"
     ]
    }
   ],
   "source": [
    "mean=luohu_data['age'].mean()\n",
    "std =luohu_data['age'].std()\n",
    "\n",
    "# 得到上下限\n",
    "lower , upper =mean -3*std , mean+3*std\n",
    "\n",
    "print('均值',mean) \n",
    "print('标准差',std) \n",
    "print('下限',lower) \n",
    "print('上限',upper)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12647366fd0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fig.set_size_inches(15,5)   # 设置画布大小\n",
    "sns.distplot(luohu_data['age'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本文微信首发于公众号【凹凸玩数据】欢迎关注查看更多更好玩的文章！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://dwz.cn/UqFdLPQQ\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "from IPython.core.display import HTML \n",
    "Image(url= \"https://dwz.cn/UqFdLPQQ\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
